|Abstract:||In this paper, a GPS interference frequency tracking and mitigation method using an adaptive linear Kalman notch filter (LKNF) is proposed to track and mitigate GPS interference signal. This LKNF has faster convergence rate than the conventional notch filtering method, and it has smaller signal loss than the conventional notch filter. Furthermore, the LKNF is more robust because it uses estimates as well as measurements. However, in order to use this method, we need to know interference frequency to estimate. Thus, time-frequency analysis method based on signal energy distribution is used. After applying this algorithm, we also implemented a multiple LKNF which is a Kalman filter with augmented states to estimate and remove multiple interference signals. In addition, in order to adjust the notch depth according to the power of the jamming signal, an adaptation logic is designed. The adaptive logic for adjusting the notch depth using signal/noise contents is based on the Q-adaptation method of adaptive Kalman filtering for indirectly adjusting a Kalman gain, which is guaranteed to be nonnegative. In order to analyze the performance of the proposed method, three Matlab-based simulations were performed. The frequency tracking and mitigation performance is compared with a conventional lattice IIR notch filter and LKNF without adaptive logic. Simulation results show that the proposed tracking and mitigation algorithm can efficiently track and eliminate interference well compared with the conventional methods.|
Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||2770 - 2779|
|Cite this article:||
Kim, Sun Young, Kang, Chang Ho, Park, Chan Gook, "Frequency Tracking and Mitigation Method of Multiple GNSS Interferences Using an Adaptive Linear Kalman Notch Filter," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2770-2779.
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